Brake system performance is critical to vehicle safety, efficiency, and sustainability — particularly as the shift to electrification brings heavier platforms and new engineering challenges. One of the most important yet complex aspects of brake design is cooling: ensuring the system can quickly dissipate heat after high-energy stops, avoiding performance fade, premature wear, and potential failure.
Traditionally, optimizing brake cooling has been a resource-intensive process. High-fidelity CFD simulations of full vehicles can take over 20 hours on hundreds of CPU cores, making it impractical to iterate extensively. Physical testing, while essential for validation, is expensive and constrained by late-stage availability of hardware. These bottlenecks limit how thoroughly engineers can explore the design space, often forcing them to compromise.
PhysicsX collaborated with a leading automotive manufacturer to change that, applying AI-driven optimization methods to dramatically accelerate brake cooling development and reduce material usage while meeting stringent performance and manufacturability requirements.
The Challenge: Better Brake Cooling Design
Brake discs in high-performance and heavy vehicles can reach temperatures exceeding 600°C in demanding conditions. Cooling performance directly influences stopping power, component life, and environmental impact. For electric vehicles, the challenge is amplified: increased mass leads to higher brake loads, while aerodynamic efficiency targets limit the available airflow for cooling.
The traditional design workflow for cooling ducts and air guides relies on trial-and-error simulation, balancing air delivery with drag penalties and packaging constraints. This project set out to:
- Increase cooling airflow to the brake disc without increasing drag, reducing the need for oversized brake discs.
- Integrate a method that could transfer learnings between vehicle variants, cutting development time and cost for subsequent platforms, and minimizing the dataset requirement to train the model.
The Approach: Active Learning & Transfer Learning
PhysicsX deployed an active learning framework powered by constrained Bayesian optimization (BO) to optimize the brake duct and air guide geometry. Instead of generating a large static dataset upfront, the AI model learned iteratively, running targeted simulations only where uncertainty was high or potential gains were greatest.
💡 Explore this cookbook as a reference for implementing various types of BO and active learning.
Key steps included:
- Geometry parameterization: Ten CAD parameters defined the duct, guide, and connecting air path. Ranges were set to respect packaging, manufacturability, and aerodynamic constraints.
- Simulation-in-the-loop: Steady CFD simulations (OpenFOAM) of the full vehicle wind tunnel setup measured airflow through the duct and across the disc. These results trained a Deep Physics Model (DPM) to predict performance in milliseconds.
- Constrained optimization: The objective was to maximize disc cooling airflow while keeping duct airflow constant to preserve drag characteristics.
- Iterative refinement: The DPM proposed the most promising designs, which were simulated, validated, and used to retrain the model.
- Transfer learning: Knowledge from the first platform’s optimization was transferred to a second vehicle variant via a platform indicator in the model. This allowed the system to capture both shared and unique characteristics of each design space.
Results: Higher Performance and Lower Cost
In the first application, BO was used to refine the brake duct and air guide geometries. The optimized design increased the mass flow of cooling air directed onto the brake disc compared to the baseline geometry, while still satisfying the constraint on duct flow. CFD visualizations showed a smoother velocity field through the duct and over the air guide, with reduced flow separation at the duct exit and increased airflow into the brake disc region. These improvements directly translated into enhanced brake cooling performance.
For the second vehicle variant, transfer learning enabled the optimization to achieve a comparable increase in cooling airflow with only half the simulation effort required in the first case. This demonstrated the method’s scalability: once trained, the model could be adapted to new platforms with significantly reduced computational cost.
Why It Works: AI Meets Engineering Reality
The success of this approach hinged on three key factors:
- Reduced virtual development cycle: DPM-based workflow significantly reduced development time compared to the CAE-based workflow.
- Active learning efficiency: A traditional surrogate-based workflow might require 200+ CFD runs to adequately sample the design space. Here, active learning achieved better results with just 50 runs for the first platform and 25 for the second.
- Engineering-integrated constraints: The optimization respected packaging envelopes, draft angles, material thickness, and aerodynamic targets from the outset, ensuring that every proposed design was production-feasible.
The resulting geometries showed subtle but meaningful improvements: smoother duct outlet curvature, better alignment between duct and guide, and reduced flow separation — all contributing to increased airflow through the disc vanes without compromising drag.
Broader Impact: Beyond Brake Cooling
While this study focused on brake cooling, the same AI-driven workflow can extend to other high-value thermal and aerodynamic challenges, from battery thermal management to HVAC systems and full-vehicle aero optimization.
The ability to:
- rapidly generate accurate AI models,
- reuse simulation data across projects, and
- integrate optimization into early-stage design
makes this approach a powerful tool for accelerating the entire vehicle development process. By reducing the number of required simulations and physical prototypes, it delivers significant cost savings, compresses timelines, and lowers the environmental footprint of engineering programs.
A Step Toward AI-Native Engineering
This project exemplifies the PhysicsX vision: embedding intelligence across the engineering lifecycle to enable faster, better, and more sustainable product development.
In the space of a few weeks, the combined engineering and AI workflow delivered:
- Substantial reduction in development time.
- Double-digit percentage performance gains in cooling airflow.
- A transferable optimization framework for future vehicle programs.
As engineering challenges grow in complexity and urgency, from electrification to climate-driven performance demands, AI-native workflows like this will be essential. By uniting deep simulation expertise with state-of-the-art machine learning, PhysicsX is enabling manufacturers to break through traditional design trade-offs, accelerating innovation in the physical world.
We’ll soon share the full technical paper, co-authored with our customer, detailing the AI workflow, simulation strategy, and key engineering decisions behind these results. It offers a deeper look at how AI-native methods accelerate development, lower costs, and unlock new performance possibilities. Stay tuned.